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Navigation of micro-swimmers in steady flow: the importance of symmetries

Published online by Cambridge University Press:  02 December 2021

Jingran Qiu Open the ORCID record for Jingran Qiu [Opens in a new window] ,
Navid Mousavi ,
Kristian Gustavsson Open the ORCID record for Kristian Gustavsson [Opens in a new window] ,
Chunxiao Xu Open the ORCID record for Chunxiao Xu [Opens in a new window] ,
Bernhard Mehlig Open the ORCID record for Bernhard Mehlig [Opens in a new window]  and
Lihao Zhao
Jingran Qiu
Affiliation:
AML, Department of Engineering Mechanics, Tsinghua University, 100084 Beijing, PR China
Navid Mousavi
Affiliation:
Department of Physics, University of Gothenburg, SE-41296 Gothenburg, Sweden
Kristian Gustavsson
Affiliation:
Department of Physics, University of Gothenburg, SE-41296 Gothenburg, Sweden
Chunxiao Xu
Affiliation:
AML, Department of Engineering Mechanics, Tsinghua University, 100084 Beijing, PR China
Bernhard Mehlig
Affiliation:
Department of Physics, University of Gothenburg, SE-41296 Gothenburg, Sweden
Lihao Zhao*
Affiliation:
AML, Department of Engineering Mechanics, Tsinghua University, 100084 Beijing, PR China
*
†Email address for correspondence: zhaolihao@mail.tsinghua.edu.cn
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Abstract

Marine micro-organisms must cope with complex flow patterns and even turbulence as they navigate the ocean. To survive they must avoid predation and find efficient energy sources. A major difficulty in analysing possible survival strategies is that the time series of environmental cues in nonlinear flow is complex and that it depends on the decisions taken by the organism. One way of determining and evaluating optimal strategies is reinforcement learning. In a proof-of-principle study, Colabrese et al. (Phys. Rev. Lett., vol. 118, 2017, 158004) used this method to find out how a micro-swimmer in a vortex flow can navigate towards the surface as quickly as possible, given a fixed swimming speed. The swimmer measured its instantaneous swimming direction and the local flow vorticity in the laboratory frame, and reacted to these cues by swimming either left, right, up or down. However, usually a motile micro-organism measures the local flow rather than global information, and it can only react in relation to the local flow because, in general, it cannot access global information (such as up or down in the laboratory frame). Here we analyse optimal strategies with local signals and actions that do not refer to the laboratory frame. We demonstrate that symmetry breaking is required to find such strategies. Using reinforcement learning, we analyse the emerging strategies for different sets of environmental cues that micro-organisms are known to measure.

JFM classification

Biological Fluid Dynamics: Micro-organism dynamics Complex Fluids: Active matter Mathematical Foundations: Machine learning
Type
JFM Papers
Information
Journal of Fluid Mechanics , Volume 932 , 10 February 2022 , A10
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Copyright
© The Author(s), 2021. Published by Cambridge University Press

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References

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